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TNO Defence, Security and Safety INVIS Integrated Night Vision Surveillance and Observation System Lex Toet & Sjaak Kriekaard

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Lecture given at Wright Patterson AFB, Dayton, Ohio, 30 September 2010

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Page 1: Wpafb 2010

TNO Defence, Security and Safety

INVIS Integrated Night Vision Surveillance and Observation System

Lex Toet & Sjaak Kriekaard

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TNO Defence, Security and Safety2

Overview

Rationale of image fusion

Color the Night: real-time sample-based natural color fusion

Previous implementations:

– Gecko = Visual + NIR

– Viper = II + LWIR

Latest development : Triclobs = Visual + NIR + LWIR

INVIS system = Triclobs + Synthetic Scene Generator +

Image Enhancement Capabilities

Example nighttime movies

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• Intensified imagery: see in the dark, context but no targetsIntensified imagery: see in the dark, context but no targets

• Thermal imagery: see through smoke, targets but no contextThermal imagery: see through smoke, targets but no context

• Fused imagery: Fused imagery: - increased information content - increased information content - best of both worlds: both targets and context- best of both worlds: both targets and context - all weather capability- all weather capability - increased system robustness- increased system robustness - decreased false alarm rate- decreased false alarm rate

Rationale of Image Fusion :

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Color-the-night

• deduces a natural color mapping from a multiband night sensor image + daytime reference image

• color mapping can be applied to other multiband images with similar content, resulting in a multiband nighttime image with natural daytime colors

• Method is:• Fast, easy to implement in real-time applications• Results in stable object colors (invariant under panning,

zooming etc).

• Natural colors lead to:• Easier and better interpretation of scene• Faster scene recognition• Better discimination & recognition of materials and objects

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Sample Based Color Transfer

color photograph visual NIR

vis + NIR recolored

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Color the Night: Applying Daytime Colors to Nighttime Imagery

ThermalImagery

Intensified Imagery

Color Reference Image

Resulting Imagery

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Visual Thermal

Grayscalefused

Color fusedimage

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Gecko: portable real time Visual + NIR fusion

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Visual + NIR Fusion: Gecko principle

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Gecko: Visual + NIR

< 700 nm

> 700 nm

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Visual Thermal

RG Gecko

NVG photo

Gecko system

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Viper: Portable Real time Visual + Thermal Fusion

eyepinhole

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Viper system: principle

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Image Intensifier Thermal camera

Intevac NightVista E1100 FLIR A10

Viper: portable real time Visual + Thermal fusion

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Viper: combined Visual + Thermal Imager

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Viper: Visual + Thermal

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Visual Thermal

RG Viper

Photo

Viper system

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Gecko bands

NV

G >

700nm

NV

G <

700nm

Daytime image

Gecko image

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Th

emal

NV

G

Viper bandsViper image

Daytime image

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The INVIS Integrated Night Vision Surveillance and Observation System

Dynamic fusion of Visual + NIR + LWIR

Real-time natural color mapping

Real-time image enhancement

Synthetic image generation

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INVIS

sensor suitelayout

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Photonis Intensified Camera Unit (ICU PP3000L)

XenICs Gobi 384 infrared microbolometer

INVIS sensors

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Viewing window

displays

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LWIR

Visual

NIR GPS - compass

Data storage

Network connection

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INVIS

mounted on all terrainmobileplatform

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Visual NIR LWIR

RGB Photo

Color the Night DNR LACE

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Color the Night

Visual NIR LWIR

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Image enhancement techniques

• Temporal noise reduction• Scene-based non-uniformity correction• Super-resolution reconstruction• Local adaptive contrast enhancement• Color-the-Night image fusion

SBNUC DSR

Color-the-Night

LACE

DSR

DSR

Resizing the TIR imageTIR

VISUAL

NIR

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Noise reductionby Dynamic Super Resolution

• Advantages• Decreased noise (improve SNR ratio)• Increased detection range• Increased situational awareness

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Fixed pattern noise reductionby Scene-Based Non-Uniformity Correction

• Advantages

• Low maintenance (hands-off operation)

• Clean pictures (performance maximized)

• Reduced sensor size / weight / cost (simpler design)

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Resolution enhancementby Dynamic Super Resolution

• Advantages• Increased sharpness • Increased situational awareness• Increased recognition and identification

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Local Adaptive Contrast Enhancement (LACE)• Advantages

• Increased situational awareness• Increased recognition and identification

in the total dynamic range

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Noise reduction

NIR VIS LWIR

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Noise reduction for I2

NormalDSR1DSR1+low LACE

NIR image

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Noise Reduction and LACE on I2 images

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Noise Reduction and LACE on LWIR images

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weak LACE vs strong LACE

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Original images, RGB fusion Original images, Color-the-Night fusion

Enhanced images, RGB fusion Enhanced images, Color-the-Night fusion

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No enhancement DSR1 + SBNUC

DSR1 + SBNUC; LACE before CtN DSR1 + SBNUC; LACE after CtN

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Color-the-Night & LACE

No LACE Low LACE before CtN Low LACE after CtN

High LACE before CtN High LACE after CtN

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Conclusions on Image Enhancement :

• Temporal Noise reduction works for all imagery• Scene Based Non Uniformity Correction (SBNUC) is useful for LWIR

• Super-resolution reconstruction has added value for LWIR• images are resolution limited

• Super-resolution reconstruction has limited value for I2

• Images are noise limited

• Enhancement algorithms need accurate motion estimation• works for small moving objects• not for scenery dominated by smoke or large moving objects

• LACE can be applied on all imagery• noisy imagery requires noise reduction

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Relation between Image Enhancement and Image Fusion

• Enhanced images can be used as input for Color-the-Night fusion

• The results have similar appearance and a significantly improved SNR

• LACE applied after tone mapping keeps the appearance similar

• LACE applied before tone mapping may enhance more details

• but is more expensive and may disturb color mapping!

• Effect of LACE applied before fusion is reduced by color mapping

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Synthetic Scene Generation

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Corresponding LWIR and synthetic imagesin 1st person perspective

LWIR Synthetic

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Corresponding LWIR and synthetic imagesin 3rd person perspective

LWIR Synthetic

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LWIR synthetic synthetic

Switching walls on --- off

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LWIR synthetic synthetic

Switching vegetation on --- off

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LWIR Material view

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Visual NIR LWIR

RGB

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RGB Photo

Color the Night DNR LACE

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RGB Synthetic scene

Color the Night DNR LACE

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Visual NIR LWIR

RGB

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Photo

Color the Night DNR LACE

RGB

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Synthetic scene

Color the Night DNR LACE

RGB

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June 2010: Data Collection Experiment on Military Base in the Netherlands

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Triclobs on scootmobile

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The Targets

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Multispectral Pinkus Target

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Real-time Scene Display

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Visual NIR

LWIR

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The End